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Yayın Construction of a Turkish proposition bank(Tubitak Scientific & Technical Research Council Turkey, 2018) Ak, Koray; Toprak, Cansu; Esgel, Volkan; Yıldız, Olcay TanerThis paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.Yayın English-Turkish parallel semantic annotation of Penn-Treebank(Oficyna Wydawnicza Politechniki Wroclawskiej, 2020) Arıcan, Bilge Nas; Bakay, Özge; Avar, Begüm; Yıldız, Olcay Taner; Ergelen, ÖzlemThis paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.Yayın On building the largest and cross-linguistic Turkish dependency corpus(Institute of Electrical and Electronics Engineers Inc., 2020-10-15) Kuzgun, Aslı; Cesur, Neslihan; Arıcan, Bilge Nas; Özçelik, Merve; Marşan, Büşra; Kara, Neslihan; Aslan, Deniz Baran; Yıldız, Olcay TanerIn this paper, we aim to introduce the dependency annotation process of the largest and the only cross-linguistic Turkish dependency treebank which was translated from the original Penn Treebank corpus. Within the scope of this project, 16.400 sentences have been morphologically and semantically annotated, and the dependency relations were manually carried out by a team of linguists. It is hoped that this project will serve as a base for a successful dependency parser and a system which can automatically perform the bi-directional conversion between constituency and dependency trees.Yayın Chunking in Turkish with conditional random fields(Springer-Verlag, 2015-04-14) Yıldız, Olcay Taner; Solak, Ercan; Ehsani, Razieh; Görgün, OnurIn this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.Yayın Evaluating the English-Turkish parallel treebank for machine translation(TÜBİTAK, 2022-01-19) Görgün, Onur; Yıldız, Olcay TanerThis study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator's behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by creating Nokia English-Turkish Treebank (NTB) to address technical document translation tasks. NTB also includes 8.3K sentences in varying lengths. We validate the corpus both extrinsically and intrinsically, and report our evaluation results regarding perplexity analysis and translation task results. Results prove that our heuristics yield promising results in terms of perplexity and are suitable for translation tasks in terms of BLEU scores.Yayın Constructing a Turkish-English parallel treebank(Association for Computational Linguistics (ACL), 2014) Yıldız, Olcay Taner; Solak, Ercan; Görgün, Onur; Ehsani, RaziehIn this paper, we report our preliminary efforts in building an English-Turkish parallel treebank corpus for statistical machine translation. In the corpus, we manually generated parallel trees for about 5,000 sentences from Penn Treebank. English sentences in our set have a maximum of 15 tokens, including punctuation. We constrained the translated trees to the reordering of the children and the replacement of the leaf nodes with appropriate glosses. We also report the tools that we built and used in our tree translation task.Yayın An all-words sense annotated Turkish corpus(IEEE, 2018-06-06) Akçakaya, Sinan; Yıldız, Olcay TanerThis paper reports our efforts in constructing of a sense labeled Turkish corpus with respect to Turkish Language Institution's dictionary, using the traditional method of manual tagging. We tagged a pre-built parallel treebank which is translated from the Penn Treebank II corpus. This approach allowed us to generate a full-coverage resource, in which syntactic and semantic information merged. We also provide miscellaneous statistics about the corpus itself as well as its development process.Yayın Creating a syntactically felicitous constituency treebank for Turkish(Institute of Electrical and Electronics Engineers Inc., 2020-10-15) Kara, Neslihan; Marşan, Büşra; Özçelik, Merve; Arıcan, Bilge Nas; Kuzgun, Aslı; Cesur, Neslihan; Aslan, Deniz Baran; Yıldız, Olcay TanerIn this study, Bakay et. al [1] and Yildiz et. al.'s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.Yayın Building annotated parallel corpora using the ATIS dataset: two UD-style treebanks in English and Turkish(European Language Resources Association (ELRA), 2024-05-20) Cesur, Neslihan; Kuzgun, Aslı; Köse, Mehmet; Yıldız, Olcay TanerIn this paper, we introduce the annotation process of the Air Travel Information Systems (ATIS) Dataset as a parallel treebank in English and in Turkish. The ATIS Dataset was originally compiled as pilot data to measure the efficiency of Spoken Language Systems and it comprises human speech transcriptions of people asking for flight information on the automated inquiry systems. Our first annotated treebank, which is in English, includes 61.879 tokens (5.432 sentences) while the second treebank, which was translated into Turkish, contains 45.875 tokens for the same amount of sentences. First, both treebanks were morphologically annotated through a semi-automatic process. Later, the dependency annotations were performed by a team of linguists according to the Universal Dependencies (UD) guidelines. These two parallel annotated treebanks provide a valuable contribution to language resources thanks to the spontaneous/spoken nature of the data and the availability of cross-linguistic dependency annotation.
